45 resultados para robust speaker verification


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Experiments and theoretical modelling have been carried out to predict the performance of a solar-powered liquid desiccant cooling system for greenhouses. We have tested two components of the system in the laboratory using MgCl2 desiccant: (i) a regenerator which was tested under a solar simulator and (ii) a desiccator which was installed in a test duct. Theoretical models have been developed for both regenerator and desiccator and gave good agreement with the experiments. The verified computer model is used to predict the performance of the whole system during the hot summer months in Mumbai, Chittagong, Muscat, Messina and Havana. Taking examples of temperate, sub-tropical, tropical and heat-tolerant tropical crops (lettuce, soya bean, tomato and cucumber respectively) we estimate the extensions in growing seasons enabled by the system. Compared to conventional evaporative cooling, the desiccant system lowers average daily maximum temperatures in the hot season by 5.5-7.5 °C, sufficient to maintain viable growing conditions for lettuce throughout the year. In the case of tomato, cucumber and soya bean the system enables optimal cultivation through most summer months. It is concluded that the concept is technically viable and deserves testing by means of a pilot installation at an appropriate location.

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MRI of fluids containing lipid coated microbubbles has been shown to be an effective toot for measuring the local fluid pressure. However, the intrinsically buoyant nature of these microbubbles precludes lengthy measurements due to their vertical migration under gravity and pressure-induced coalescence. A novel preparation is presented which is shown to minimize both these effects for at least 25 min. By using a 2% polysaccharide gel base with a small concentration of glycerol and 1,2-distearoyl-sn-glycero-3-phosphocholine coated gas microbubbles, MR measurements are made for pressures between 0.95 and 1.44 bar. The signal drifts due to migration and amalgamation are shown to be minimized for such an experiment whilst yielding very high NMR sensitivities up to 38% signal change per bar.

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We experimentally confirm the optimum combination of modulator delay and filter bandwidth to maximize the dispersion tolerance of partial DPSK.

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Recently introduced surface nanoscale axial photonics (SNAP) makes it possible to fabricate high-Q-factor microresonators and other photonic microdevices by dramatically small deformation of the optical fiber surface. To become a practical and robust technology, the SNAP platform requires methods enabling reproducible modification of the optical fiber radius at nanoscale. In this Letter, we demonstrate superaccurate fabrication of high-Q-factor microresonators by nanoscale modification of the optical fiber radius and refractive index using CO laser and UV excimer laser beam exposures. The achieved fabrication accuracy is better than 2Å in variation of the effective fiber radius. © 2011 Optical Society of America.

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

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On the basis of the standard model for the photorefractive nonlinearity we investigate whether a systematic description of the dependence of two-beam energy exchange on beam polarization and grating vector K is possible. Our result is that there is good agreement between theory and experiment with respect to the polarization properties and semi-quantitative agreement with respect to the K-dependence of the energy exchange.

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We introduce two techniques to measure the efficiency of inter mode FWM with respect to intra mode FWM. The first technique allows an estimation of the additional FWM penalty for any given system; the second isolates the contribution of each mode. Measurements are compared to an analytical model showing the FWM signal increases by ∼2dB with inter mode phase matching.

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The focus of our work is the verification of tight functional properties of numerical programs, such as showing that a floating-point implementation of Riemann integration computes a close approximation of the exact integral. Programmers and engineers writing such programs will benefit from verification tools that support an expressive specification language and that are highly automated. Our work provides a new method for verification of numerical software, supporting a substantially more expressive language for specifications than other publicly available automated tools. The additional expressivity in the specification language is provided by two constructs. First, the specification can feature inclusions between interval arithmetic expressions. Second, the integral operator from classical analysis can be used in the specifications, where the integration bounds can be arbitrary expressions over real variables. To support our claim of expressivity, we outline the verification of four example programs, including the integration example mentioned earlier. A key component of our method is an algorithm for proving numerical theorems. This algorithm is based on automatic polynomial approximation of non-linear real and real-interval functions defined by expressions. The PolyPaver tool is our implementation of the algorithm and its source code is publicly available. In this paper we report on experiments using PolyPaver that indicate that the additional expressivity does not come at a performance cost when comparing with other publicly available state-of-the-art provers. We also include a scalability study that explores the limits of PolyPaver in proving tight functional specifications of progressively larger randomly generated programs. © 2014 Springer International Publishing Switzerland.

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A combination of the two-fluid and drift flux models have been used to model the transport of fibrous debris. This debris is generated during loss of coolant accidents in the primary circuit of pressurized or boiling water nuclear reactors, as high pressure steam or water jets can damage adjacent insulation materials including mineral wool blankets. Fibre agglomerates released from the mineral wools may reach the containment sump strainers, where they can accumulate and compromise the long-term operation of the emergency core cooling system. Single-effect experiments of sedimentation in a quiescent rectangular column and sedimentation in a horizontal flow are used to verify and validate this particular application of the multiphase numerical models. The utilization of both modeling approaches allows a number of pseudocontinuous dispersed phases of spherical wetted agglomerates to be modeled simultaneously. Key effects on the transport of the fibre agglomerates are particle size, density and turbulent dispersion, as well as the relative viscosity of the fluid-fibre mixture.

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The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

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Master of Arts dissertation

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Recently introduced surface nanoscale axial photonics (SNAP) makes it possible to fabricate high-Q-factor microresonators and other photonic microdevices by dramatically small deformation of the optical fiber surface. To become a practical and robust technology, the SNAP platform requires methods enabling reproducible modification of the optical fiber radius at nanoscale. In this Letter, we demonstrate superaccurate fabrication of high-Q-factor microresonators by nanoscale modification of the optical fiber radius and refractive index using CO laser and UV excimer laser beam exposures. The achieved fabrication accuracy is better than 2Å in variation of the effective fiber radius. © 2011 Optical Society of America.

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Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

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Reliability modelling and verification is indispensable in modern manufacturing, especially for product development risk reduction. Based on the discussion of the deficiencies of traditional reliability modelling methods for process reliability, a novel modelling method is presented herein that draws upon a knowledge network of process scenarios based on the analytic network process (ANP). An integration framework of manufacturing process reliability and product quality is presented together with a product development and reliability verification process. According to the roles of key characteristics (KCs) in manufacturing processes, KCs are organised into four clusters, that is, product KCs, material KCs, operation KCs and equipment KCs, which represent the process knowledge network of manufacturing processes. A mathematical model and algorithm is developed for calculating the reliability requirements of KCs with respect to different manufacturing process scenarios. A case study on valve-sleeve component manufacturing is provided as an application example of the new reliability modelling and verification procedure. This methodology is applied in the valve-sleeve component manufacturing processes to manage and deploy production resources.